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Record W1892078110 · doi:10.1525/cond.2012.110112

Nest Attendance and Reproductive Success in the Wood Thrush

2012· article· en· W1892078110 on OpenAlexafffund
Melissa L. Evans, Bridget J. M. Stutchbury

Bibliographic record

VenueOrnithological Applications · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversité LavalYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsNest (protein structural motif)PasserinePaternal careAttendanceReproductive successBiologyEcologyNesting (process)OffspringDemographyReproductionZoologyPregnancyPopulation

Abstract

fetched live from OpenAlex

For most bird species, biparental care is expected to play an integral role in offspring survival. Nevertheless, relatively few studies have simultaneously examined male and female investment into nest attendance, the prevalence of nest attendance through the nesting cycle, or the relationship between nest-attendance effort and nesting success. Here, in a monogamous passerine, the Wood Thrush (Hylocichla mustelina), we use radiotracking to examine nest-attendance behaviors of males and females during the laying, incubation, and nestling stages, and the relationships between nest attendance and nesting success. Across nesting stages, males spent between 14–38% of their time <5 m and 21–58% of their time 5–25 m from the nest, and the time males spent near the nest was positively associated with nesting success. During the incubation stage, the amount of time males spent <5 m from the nest depended upon the female's presence on or off of the nest, as males appear to coordinate their nestattendance behaviors with females. Overall, our results indicate that male Wood Thrushes invest extensively into indirect parental-care behaviors throughout the nesting cycle and that increased nest attendance translates into improved offspring survival.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.269
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2012
Admission routes2
Has abstractyes

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